Resolution-Agnostic Remote Sensing Scene Classification With Implicit Neural Representations
Keyan Chen, Wenyuan Li, Jianqi Chen, Zhengxia Zou, Zhenwei Shi
Abstract
Remote sensing scene classification is an important yet challenging task. In recent years, the excellent feature representation ability of convolutional neural networks (CNNs) has led to substantial improvements in scene classification accuracy. However, handling resolution variations of remote sensing images is still challenging because CNNs are not inherently capable of modeling multiresolution input images. In this letter, we propose a novel scene classification method with scale and resolution adaptation ability by leveraging the recent advances in implicit neural representations (INRs). Unlike previous CNN-based methods that make predictions based on rasterized image inputs, the proposed method converts the images as continuous functions with INRs optimization and then performs classification within the function space. When the image is represented as a function, the image resolution can be decoupled from the pixel values so that the resolution does not have much impact on the classification performance. Our method also shows great potential for multiresolution remote sensing scene classification. Using only a simple multilayer perceptron (MLP) classifier in the proposed function space, our method achieves classification accuracy comparable to deep CNNs but exhibits better adaptability to image scale and resolution changes.